CN112348812B - Forest stand age information measurement method and device - Google Patents
Forest stand age information measurement method and device Download PDFInfo
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Abstract
The invention relates to a method and a device for measuring stand age information, which are used for obtaining corresponding ground surface true reflectivity according to remote sensing satellite images of a stand to be measured in a long-time sequence; further, a long-time sequence normalized vegetation index is obtained according to the ground surface true reflectivity, a stand age model is built according to the normalized vegetation index, and stand ages of the stand to be measured are traced back and inverted according to the stand age model. Based on the method, the remote sensing satellite image is used for finishing the retrospective inversion of the forest stand ages of the forest stand to be measured, and the forest stand ages of the forest stand to be measured are measured efficiently, so that the large-area forest age information drawing and forest dynamic change tracking work can be conveniently realized.
Description
Technical Field
The invention relates to the technical field of forest metering research, in particular to a method and a device for measuring forest stand age information.
Background
The forest ecosystem is taken as an important component of the global land ecosystem, the carbon reserves of the forest ecosystem account for about 33% -46% of the total carbon reserves of the global land ecosystem, and the forest ecosystem is taken as the largest carbon reservoir of the world, and plays an important role in regulating the carbon balance space-time distribution pattern of the global ecosystem, regulating global climate change and promoting human sustainable development.
The forest stand refers to a piece of forest with the same internal structural characteristics such as tree origin, tree species composition, forest phase, forest age, canopy density, status level or status index, yield, forest, lin Kuang and the like, and is obviously different from the surrounding, and is an organic entity of forest plant communities and site conditions, is also an entity of forest and forest manager, and the forest stand age is the average age of the forest in the forest. The age of the forest stand is taken as one of parameters for evaluating the health condition of forest growth, and the accurate estimation of the age of the forest stand is related to the scientificity of the policy making of the guidelines by the forestry department, and can directly influence the remote sensing quantitative inversion and model estimation accuracy of parameters such as the carbon reserves of forest vegetation, the net vegetation productivity (NPP), the forest accumulation and the like.
The traditional method for acquiring the forest stand age is a growth cone method and a history tracking method, and the accuracy of the forest stand age result obtained by the two methods is higher. However, since the stand is generally a large-area forest, the conventional stand age acquisition method needs to consume a large amount of manpower and material resources, and is more difficult to deal with the large-area stand age investigation. It can be seen that the conventional stand age acquisition method has the above drawbacks.
Disclosure of Invention
Based on the above, it is necessary to provide a method and a device for measuring stand age information, aiming at the defects of the traditional stand age acquisition method.
A stand age information measuring method comprises the following steps:
Acquiring remote sensing satellite images of a forest stand to be measured in a long-time sequence, and acquiring corresponding ground surface true reflectivity according to each remote sensing satellite image;
Obtaining a long-time sequence normalized vegetation index according to the ground surface true reflectivity;
And constructing a stand age model according to the normalized vegetation index, and tracing and inverting the stand age of the stand to be measured according to the stand age model.
According to the forest stand age information measurement method, after remote sensing satellite images of the forest stand to be measured are obtained in a long-time sequence, corresponding ground surface true reflectivity is obtained according to each remote sensing satellite image; further, a long-time sequence normalized vegetation index is obtained according to the ground surface true reflectivity, a stand age model is built according to the normalized vegetation index, and stand ages of the stand to be measured are traced back and inverted according to the stand age model. Based on the method, the remote sensing satellite image is used for finishing the retrospective inversion of the forest stand ages of the forest stand to be measured, and the forest stand ages of the forest stand to be measured are measured efficiently, so that the large-area forest age information drawing and forest dynamic change tracking work can be conveniently realized.
In one embodiment, the process of obtaining the corresponding ground surface true reflectivity according to each remote sensing satellite image includes the steps of:
Converting DN value of the remote sensing satellite image into a radiation brightness value;
And obtaining the true reflectivity of the earth surface according to the radiation brightness value.
In one embodiment, the process of converting the DN value of the remote sensing satellite image into the radiance value is as follows:
Lra＝DN*Gain+Offset
Wherein L ra is a radiance value, gain is a correction Gain coefficient, offset is a correction Offset, and DN is a DN value.
In one embodiment, the process of obtaining the true reflectivity of the earth's surface from the radiance value is as follows:
Wherein p is the true reflectivity of the ground, L ra is the radiation brightness value, L path is the range radiation, E is the radiance of the ground object, and τ is the atmospheric transmittance.
In one embodiment, a process for obtaining a long-time series of normalized vegetation indices from the true reflectivity of the earth's surface is as follows:
Wherein NDVI is a normalized vegetation index, ρ red is a reflectance value of a red band, and ρ nir is a reflectance value of a near infrared band.
In one embodiment, before the process of constructing the stand age model according to the normalized vegetation index, the method further comprises the steps of:
and correcting abnormal pixel values in the remote sensing satellite images.
In one embodiment, before the process of constructing the stand age model according to the normalized vegetation index, the method further comprises the steps of:
And correcting the normalized vegetation indexes acquired by different satellite sensors through a linear regression model.
In one embodiment, the process of correcting the normalized vegetation index acquired by different satellite sensors by a linear regression model is as follows:
wherein y=ax+b is the linear change of the corrected normalized vegetation index, The image mean values of different satellite sensors in the same year are respectively shown, and sigma y、σx is the data standard deviation of the different satellite sensors in the same year.
In one embodiment, before the process of constructing the stand age model according to the normalized vegetation index, the method further comprises the steps of:
and filtering and smoothing the normalized vegetation index.
In one embodiment, the filtering and smoothing process for the normalized vegetation index comprises the steps of:
and filtering and smoothing the normalized vegetation index through a Savitzky-Golay filter, wherein the following formula is as follows:
wherein, For reconstructed data, y j+i is the original data, C i is the filter coefficient, N is the number of data of the sliding window under study, k is the width of the window, and n=2k+1.
In one embodiment, a stand age model is constructed according to the normalized vegetation index, and a stand age process of the stand to be measured is traced back and inverted according to the stand age model, wherein the formula is as follows:
Wherein M is the age of the stand to be measured, T c is the felling node, T p is the planting node, To normalize vegetation index at felling node,/>For normalized vegetation index at planting node, NDVI i is the normalized vegetation index of the long-time series,/>Is the maximum value of normalized vegetation index of long time sequence,/>Is the minimum value of normalized vegetation index of long time sequence,/>Deriving a first derivative value at the felling node after a first order differentiation for a normalized vegetation index based on a long time series,/>Deriving a first derivative value at the planting node after a first derivative for a normalized vegetation index set based on the long time series; the felling node is the point at which the normalized vegetation index starts to change from a high value to a low value and shows a continuous descending trend, and the planting node is the lowest point of the normalized vegetation index of the long-time sequence.
A stand age information measuring apparatus comprising:
The image acquisition module is used for acquiring remote sensing satellite images of the forest stand to be detected in a long-time sequence and acquiring corresponding ground surface true reflectivity according to each remote sensing satellite image;
The index acquisition module is used for acquiring a long-time sequence normalized vegetation index according to the ground surface true reflectivity;
the age measurement module is used for constructing a stand age model according to the normalized vegetation index and tracing and inverting the stand age of the stand to be measured according to the stand age model.
According to the stand age information measuring device, after remote sensing satellite images of stand to be measured are obtained in a long-time sequence, corresponding ground surface true reflectivity is obtained according to each remote sensing satellite image; further, a long-time sequence normalized vegetation index is obtained according to the ground surface true reflectivity, a stand age model is built according to the normalized vegetation index, and stand ages of the stand to be measured are traced back and inverted according to the stand age model. Based on the method, the remote sensing satellite image is used for finishing the retrospective inversion of the forest stand ages of the forest stand to be measured, and the forest stand ages of the forest stand to be measured are measured efficiently, so that the large-area forest age information drawing and forest dynamic change tracking work can be conveniently realized.
A computer storage medium having stored thereon computer instructions which, when executed by a processor, implement the stand age information measurement method of any of the above embodiments.
The computer storage medium acquires remote sensing satellite images of the forest stand to be measured in a long-time sequence, and acquires corresponding ground surface true reflectivity according to each remote sensing satellite image; further, a long-time sequence normalized vegetation index is obtained according to the ground surface true reflectivity, a stand age model is built according to the normalized vegetation index, and stand ages of the stand to be measured are traced back and inverted according to the stand age model. Based on the method, the remote sensing satellite image is used for finishing the retrospective inversion of the forest stand ages of the forest stand to be measured, and the forest stand ages of the forest stand to be measured are measured efficiently, so that the large-area forest age information drawing and forest dynamic change tracking work can be conveniently realized.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the stand age information measurement method of any of the above embodiments when the program is executed by the processor.
The computer equipment acquires remote sensing satellite images of the forest stand to be measured in a long-time sequence, and acquires corresponding ground surface true reflectivity according to each remote sensing satellite image; further, a long-time sequence normalized vegetation index is obtained according to the ground surface true reflectivity, a stand age model is built according to the normalized vegetation index, and stand ages of the stand to be measured are traced back and inverted according to the stand age model. Based on the method, the remote sensing satellite image is used for finishing the retrospective inversion of the forest stand ages of the forest stand to be measured, and the forest stand ages of the forest stand to be measured are measured efficiently, so that the large-area forest age information drawing and forest dynamic change tracking work can be conveniently realized.
Drawings
FIG. 1 is a flowchart of a method for measuring forest stand age information according to an embodiment;
FIG. 2 is a flowchart of a method for measuring stand age information according to another embodiment;
FIG. 3 is a schematic diagram of a method for performing radiation normalization and NDVI dataset construction on Landsat series remote sensing satellite images according to an application example;
FIG. 4 is a diagram illustrating a method for checking the correctness of an NDVI dataset according to an embodiment;
FIG. 5 is a flowchart of a method for measuring stand age information according to still another embodiment;
FIG. 6 is a timing diagram of the NDVI values before correction of a Landsat satellite according to an embodiment;
FIG. 7 is a timing diagram of corrected NDVI values for a Landsat satellite according to an embodiment;
FIG. 8 is a schematic diagram of a Savitzky-Golay filter smoothing method;
fig. 9 is a schematic diagram of a method for retrospectively extracting pinus koraiensis by using a stand model according to an application example;
FIG. 10 is a schematic diagram of a stand model retrospective inversion result for an application example;
Fig. 11 is a block diagram of a stand age information measuring apparatus according to an embodiment.
Detailed Description
For a better understanding of the objects, technical solutions and technical effects of the present invention, the present invention will be further explained below with reference to the drawings and examples. Meanwhile, it is stated that the embodiments described below are only for explaining the present invention and are not intended to limit the present invention.
The embodiment of the invention provides a method for measuring stand age information.
Fig. 1 is a flowchart of a stand age information measurement method according to an embodiment, as shown in fig. 1, the stand age information measurement method according to an embodiment includes steps S100 to S102:
S100, acquiring remote sensing satellite images of a forest stand to be measured in a long-time sequence, and acquiring corresponding ground surface true reflectivity according to each remote sensing satellite image;
The satellite remote sensing technology has the characteristics of real-time, rapid and large-area synchronous monitoring, and has unique advantages in describing dynamic change characteristics of the earth surface and ground features in time and space dimensions. The current remote sensing satellites gradually develop from low-spatial resolution satellites (such as MODIS, medium-high spatial resolution satellites Landsat series, sentinel-2 and domestic GF series satellites) to high-resolution spectrum satellites (WorldView-2 and QuickBird), so that the spectral dynamic response characteristics of the remote sensing data on different spatial and time scales are mined, and a technical basis is provided for estimating and monitoring the forest ages of the dominant forest tree species.
Based on the remote sensing satellite image acquisition of the calendar year, a long-time sequence propelled by the year is determined, and the remote sensing satellite image of the forest stand to be measured based on the long-time sequence is acquired. The normalized vegetation index of the subsequently formed time series is presented in the form of a curve or set.
In one example, fig. 2 is a flowchart of a stand age information measurement method according to another embodiment, as shown in fig. 2, in step S100, a process of obtaining a corresponding ground surface true reflectivity according to each remote sensing satellite image includes step S200 and step S201:
S200, converting DN values of the remote sensing satellite images into radiation brightness values;
The brightness value of the brightness gray DN (Digital Number remote sensing image pixel brightness value) value of each image in the remote sensing satellite influence is radiated to finish the radiation calibration.
In one embodiment, in step S200, the process of converting the DN value of the remote sensing satellite image into the radiance value is as follows:
Lra＝DN*Gain+Offset
Wherein L ra is a radiance value, gain is a correction Gain coefficient, offset is a correction Offset, and DN is a DN value.
S201, obtaining the true reflectivity of the earth surface according to the radiation brightness value.
The radiation brightness value is converted into the ground surface true reflectivity so as to complete the process of atmospheric correction.
In one embodiment, the process of obtaining the true reflectivity of the earth according to the radiance value in step S201 is as follows:
Wherein p is the true reflectivity of the ground, L ra is the radiation brightness value, L path is the range radiation, E is the radiance of the ground object, and τ is the atmospheric transmittance.
As a preferred embodiment, the technical solution of the present embodiment is better explained. Taking a forest stand to be measured as an artificial oil pine as an example, application examples of the steps of the technical scheme of the embodiment are shown. Fig. 3 is a schematic diagram of a method for performing image radiation normalization and NDVI (Normalized Difference Vegetation Index normalized vegetation index) dataset construction on a Landsat series remote sensing satellite according to an application example, as shown in fig. 3, in which the application example adopts a terrestrial satellite ecosystem interference adaptive processing system LEDAPS (Landsat Ecosystem Disturbance Adaptive Processing System) software developed by the United States Geological Survey (USGS) earth resource observation and scientific center scientific processing department (ESPA). The LEDAPS item mainly researches a preprocessing algorithm of a long-span dense time sequence Landsat GeoCover image dataset, and the specific implementation flow is as follows: firstly, correcting an original spectrum signal value into an atmosphere layer top reflectivity by using an atmosphere correction MODTRA model; then performing cloud detection and cloud mask operation; performing orthorectification and accurate registration on the remote sensing image by utilizing AROP program package algorithm; and finally, performing atmospheric correction on the image data after radiometric calibration and cloud masking by using a 6S radiation transmission model, so that the spectrum value of the remote sensing image is converted into the ground surface true reflectivity, and a ground surface reflectivity product is formed.
S101, obtaining a normalized vegetation index of a long-time sequence according to the true reflectivity of the earth surface;
After obtaining the earth surface true reflectivity of the long-time sequence in the step S100 and calculating the normalized vegetation index of the long-time sequence, converting the earth surface true reflectivity of the long-time sequence into the normalized vegetation index according to the nodes to obtain the normalized vegetation index of the long-time sequence. The normalized vegetation index of the long-time sequence can be presented in an NDVI curve, the NDVI curve is a time-varying signal formed by an NDVI long-time sequence data set, and the time-varying signal presents periods and variations related to vegetation biological characteristics according to the difference of the surface coverage types, and presents definite annual and seasonal variations.
In one embodiment, the process of obtaining a long-time series of normalized vegetation indexes according to the true reflectivity of the earth' S surface in step S101 is as follows:
Wherein NDVI is a normalized vegetation index, ρ red is a reflectance value of a red band, and ρ nir is a reflectance value of a near infrared band.
And according to the formula, the normalized vegetation index calculation of each node is completed, and the normalized vegetation index of the long-time sequence is determined.
In the application example of the embodiment, the Chinese pine belongs to evergreen needle woods, and in order to avoid the influence of complex ground features on spectrum information of the Chinese pine, and simultaneously, the distribution area information of the Chinese pine is rapidly extracted, and the selected image is mainly an NDVI image in winter. Finally, the time of acquiring the long-time sequence remote sensing satellite image data is from 1984 to 2019, and the span is 35 years. Different sensors, mist and other atmospheric conditions, sun position and angle conditions and certain unavoidable noise can cause information mismatch between the measured value of the sensor and the reflectivity of the target ground object. Information mismatch is common in time series data spectra, which can lead to outliers in the time series spectra for features. In order to reduce the problem of mismatch of time series spectrum information, the invention evaluates the correctness of time series data by using the NDVI time sequence change of two types of fixed ground objects (including an airport runway and bare soil). Fig. 4 is a schematic diagram of an application example NDVI data set correctness checking method, and as shown in fig. 4, an analysis result shows that the NDVI value of the airfield runway in the left graph always changes within the range of 0-0.1, the NDVI value of the bare soil in the right graph always changes within the range of 0.1-0.2, the NDVI time sequence change difference of the two types of fixed ground objects is small, and no abnormal change occurs. Therefore, the time series data has higher accuracy degree, and can be used for tracking the time series change rule of the pinus sylvestris.
In one embodiment, fig. 5 is a flowchart of a stand age information measurement method according to another embodiment, as shown in fig. 5, before the process of constructing a stand age model according to the normalized vegetation index in step S102, the method further includes step S300:
s300, correcting abnormal pixel values in the remote sensing satellite images.
Because the remote sensing satellite image data is influenced by objective environmental factors such as clouds, shadows, rain and snow and the like, the pixels of part of the remote sensing satellite image data are easy to be abnormal. The abnormal pixel values can be removed through an acquisition tool of the remote sensing satellite image so as to be completely corrected.
In one embodiment, the removed pels are modified using pel information from adjacent years. As a preferred embodiment, the outlier pixels are 0-valued pixels, the 0-valued pixels of the first and last year being replaced with non-0-valued pixels of the last year, the 0-valued pixels of the other year being replaced with the average of the pixels of the adjacent year.
In the application example, problematic pixels are removed by a quality evaluation Band (The Quality Assessment (QA) Band) generated after LEDAPS batches, and cloud, shadow and snow mask products are extracted from the Band. The quality evaluation wave band (QA) can indicate which pixel is possibly affected by cloud layer or instrument, and the problematic pixel is selected from normal pixels, so that the rigor of scientific research can be ensured, and the reliability of long-time sequence data research caused by non-human factors is reduced.
In one embodiment, as shown in fig. 5, before the process of constructing the stand age model according to the normalized vegetation index in step S102, the method further includes step S400:
s400, correcting normalized vegetation indexes acquired by different satellite sensors through a linear regression model.
The acquired remote sensing satellite images are acquired in a long time sequence, and if a satellite sensor for acquiring the remote sensing satellite images is replaced in the process, the remote sensing satellite images before and after the time node is replaced can be affected. Taking an application example of a forest stand as an artificial oil pine forest as an example, fig. 6 is a timing diagram of NDVI values before correction of a Landsat satellite in an application example, and as shown in fig. 6, the NDVI values are obviously raised after the NDVI values are calculated by combining an OLI wave band to form a long time sequence NDVI by analyzing the NDVI change rule of the pine from a young forest to a mature forest by selecting 32 years, 40 years, 45 years, 50 years and 55 years of ages of forest. After further analysis, it was considered that the possible reason was that the red band (0.63-0.69 μm) and near infrared band (0.76-0.90 μm) spectral ranges were identical in the Landsat series of TM and ETM+ data and that the narrowing of the red band (0.63-0.68 μm) and near infrared band (0.845-0.875 μm) spectral channels of the OLI data resulted in the use of the OLI data to calculate the NDVI values, high values were present.
In one embodiment, the process of correcting the normalized vegetation index acquired by the different satellite sensors in step S400 by the linear regression model is as follows:
wherein y=ax+b is the linear change of the corrected normalized vegetation index, The image mean values of different satellite sensors in the same year are respectively shown, and sigma y、σx is the data standard deviation of the different satellite sensors in the same year.
In the application example, σ y、σx represents the standard deviation of the OLI whole image NDVI data in 2013 and the standard deviation of the etm+whole image NDVI data in 2013 respectively, and the calculated a value is 0.67 and b value is 25.70, so that the final linear regression formula is as follows:
y＝0.67x+25.70
it should be noted that the above-mentioned linear values a and b may be calculated according to different application scenarios, and the above-mentioned fixed values are only explained by application examples and do not represent the only limitation.
Fig. 7 is a corrected NDVI value time sequence change chart of a Landsat satellite in an application example, as shown in fig. 7, the graph result shows that the converted NDVI curve shows a normal NDVI change trend, and the ndi raised area calculated by OLI obviously shows a change trend similar to TM and etm+ data, and the trend better accords with the time sequence change trend of the pinus sylvestris age.
In one embodiment, as shown in fig. 5, before the process of constructing the stand age model according to the normalized vegetation index in step S102, the method further includes step S500:
S500, filtering and smoothing the normalized vegetation index.
The normalized vegetation index is filtered through the filter, so that the NDVI curve is smoother and richer in detail, and the method is more suitable for research deduction of forest stand ages.
In one embodiment, the filtering smoothing process of the normalized vegetation index in step S500 includes the steps of:
and filtering and smoothing the normalized vegetation index through a Savitzky-Golay filter, wherein the following formula is as follows:
wherein, For reconstructed data, y j+i is the original data, C i is the filter coefficient, N is the number of data of the sliding window under study, k is the width of the window, and n=2k+1.
The Savitzky-Golay filter filtering is a weighted average algorithm based on a moving window, the algorithm core is to fit a polynomial through a fixed number of points close to a certain point, and the optimal smooth value of the point is obtained through continuous iteration of the polynomial.
In an application example, fig. 8 is a schematic diagram of a Savitzky-Golay filtering smoothing method, and as shown in fig. 8, savitzky-Golay filtering needs to set two parameters, namely, a smoothing window size and a degree of a smoothing polynomial. The size of the filter window can affect the smoothed data result, with the larger the window width, the smoother the data. The degree of the smoothing polynomial affects the filtering details, the greater the degree, the clearer the texture details. The normalized vegetation index of the long-time sequence reconstructed by the filter can accurately describe local mutation information and long-time variation trend of the time sequence, and is not limited by a sensor and a data space-time scale. Depending on the advantages of the filter and the experimental parameters, in one embodiment the moving window is set to 5 x 5 and the degree of the smoothing polynomial is set to 2. The normalized vegetation index of the long-time sequence is subjected to filtering transformation through Savitzky-Golay filtering, so that the spectrum change characteristics of the Pinus koraiensis for many years are shown. Compared with the disorder of the normalized long-time series NDVI data, the filtered NDVI change curve of the different forest ages of Chinese pine is smoother and has more abundant details, and is more suitable for researching the forest ages.
S102, constructing a stand age model according to the normalized vegetation index, and tracing and inverting the stand age of the stand to be detected according to the stand age model.
And performing retrospective inversion of forest stand ages by using a time sequence normalized vegetation index Hou Jianlin-point age model.
In one embodiment, a stand age model is constructed according to the normalized vegetation index, and a stand age process of the stand to be measured is traced back and inverted according to the stand age model, wherein the formula is as follows:
Wherein M is the age of the stand to be measured, T c is the felling node, T p is the planting node, To normalize vegetation index at felling node,/>For normalized vegetation index at planting node, NDVI i is the normalized vegetation index of the long-time series,/>Is the maximum value of normalized vegetation index of long time sequence,/>Is the minimum value of normalized vegetation index of long time sequence,/>Deriving a first derivative value at the felling node after a first order differentiation for a normalized vegetation index based on a long time series,/>Deriving a first derivative value at the planting node after a first derivative for a normalized vegetation index set based on the long time series; the felling node is the point at which the normalized vegetation index starts to change from a high value to a low value and shows a continuous descending trend, and the planting node is the lowest point of the normalized vegetation index of the long-time sequence.
Fig. 9 is a schematic diagram of a method for extracting pinus koraiensis by tracing a stand model of an application example, and as shown in fig. 9, 4 representative pinus koraiensis ages are selected for constructing a time series of artificial planting forest age models of pinus koraiensis, including 15 years, 32 years and 45 years of pinus koraiensis and felling areas. The NDVI starts to grow continuously in the process of growing from young woods to mature woods, and shows a relatively stable trend after reaching 0.4. When the Chinese pine is cut in a large area, the NDVI is continuously reduced, the NDVI value (about 0.2) similar to that of bare soil is kept if the Chinese pine is not planted any more, when the Chinese pine is planted again after the Chinese pine is cut in a large area, the NDVI value of the Chinese pine is approximately 6-9 years to reach 0.4 again, and the NDVI is relatively stable. According to the information and by combining specific forest age information, the lowest point of the time series NDVI is defined as a planting node T p, and the NDVI value is the minimum value in the time series and gradually rises after the NDVI value is the minimum value in the time series until the NDVI is stabilized above 0.4; the point at which NDVI starts to change from a high value to a low value and shows a decreasing trend is defined as the felling node T c. And tracing the age of the planting forest of the Chinese pine according to the planting node and felling node information.
Based on this, fig. 10 is a schematic diagram of a stand model retrospective inversion result of an application example, and as shown in fig. 10, the left graph is a pinus koraiensis planting distribution, and the right graph is an pinus koraiensis age inversion result. Therefore, the mode of the embodiment of the invention can effectively cope with the forest stand age measurement of a large-area forest.
According to the stand age information measurement method of any embodiment, after remote sensing satellite images of stand to be measured with long-time sequences are obtained, corresponding ground surface true reflectivity is obtained according to each remote sensing satellite image; further, a long-time sequence normalized vegetation index is obtained according to the ground surface true reflectivity, a stand age model is built according to the normalized vegetation index, and stand ages of the stand to be measured are traced back and inverted according to the stand age model. Based on the method, the remote sensing satellite image is used for finishing the retrospective inversion of the forest stand ages of the forest stand to be measured, and the forest stand ages of the forest stand to be measured are measured efficiently, so that the large-area forest age information drawing and forest dynamic change tracking work can be conveniently realized.
Meanwhile, the NDVI value difference calculated by different sensors of the satellite is effectively reduced through abnormal pixel value correction, linear regression model correction and filtering smoothing treatment, and the calculation efficiency of an algorithm is effectively improved while the stand age inversion measurement accuracy is ensured.
The embodiment of the invention also provides a stand age information measuring device.
Fig. 11 is a block diagram of a stand age information measurement device according to an embodiment, and as shown in fig. 11, stand age information measurement according to an embodiment includes a block 100, a block 101, and a block 102:
the image acquisition module 100 is used for acquiring remote sensing satellite images of the forest stand to be measured in a long-time sequence and acquiring corresponding ground surface true reflectivity according to each remote sensing satellite image;
the index acquisition module 101 is used for acquiring a long-time sequence normalized vegetation index according to the true reflectivity of the earth surface;
the age measurement module 102 is configured to construct a stand age model according to the normalized vegetation index, and trace and invert the stand age of the stand to be measured according to the stand age model.
According to the stand age information measuring device, after remote sensing satellite images of stand to be measured are obtained in a long-time sequence, corresponding ground surface true reflectivity is obtained according to each remote sensing satellite image; further, a long-time sequence normalized vegetation index is obtained according to the ground surface true reflectivity, a stand age model is built according to the normalized vegetation index, and stand ages of the stand to be measured are traced back and inverted according to the stand age model. Based on the method, the remote sensing satellite image is used for finishing the retrospective inversion of the forest stand ages of the forest stand to be measured, and the forest stand ages of the forest stand to be measured are measured efficiently, so that the large-area forest age information drawing and forest dynamic change tracking work can be conveniently realized.
The embodiment of the invention also provides a computer storage medium, on which computer instructions are stored, which when executed by a processor, implement the stand age information measurement method of any of the above embodiments.
Those skilled in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware associated with program instructions, where the foregoing program may be stored in a computer readable storage medium, and when executed, the program performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a random access Memory (RAM, random Access Memory), a Read-Only Memory (ROM), a magnetic disk or an optical disk, or the like, which can store program codes.
Or the above-described integrated units of the invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present invention may be essentially or part contributing to the related art, and the computer software product may be stored in a storage medium, and include several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program code, such as a removable storage device, RAM, ROM, magnetic or optical disk.
Corresponding to the above computer storage medium, in one embodiment, there is also provided a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements any of the stand age information measurement methods of the above embodiments when executing the program.
The computer equipment acquires remote sensing satellite images of the forest stand to be measured in a long-time sequence, and acquires corresponding ground surface true reflectivity according to each remote sensing satellite image; further, a long-time sequence normalized vegetation index is obtained according to the ground surface true reflectivity, a stand age model is built according to the normalized vegetation index, and stand ages of the stand to be measured are traced back and inverted according to the stand age model. Based on the method, the remote sensing satellite image is used for finishing the retrospective inversion of the forest stand ages of the forest stand to be measured, and the forest stand ages of the forest stand to be measured are measured efficiently, so that the large-area forest age information drawing and forest dynamic change tracking work can be conveniently realized.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (6)
1. The method for measuring the forest stand age information is characterized by comprising the following steps:
Acquiring remote sensing satellite images of a forest stand to be measured in a long-time sequence, and acquiring corresponding ground surface true reflectivity according to each remote sensing satellite image;
The process for obtaining the corresponding ground surface true reflectivity according to each remote sensing satellite image comprises the following steps:
Converting DN value of the remote sensing satellite image into a radiation brightness value;
Obtaining the true reflectivity of the earth surface according to the radiation brightness value;
the process of converting DN value of the remote sensing satellite image into radiation brightness value is as follows:
Lra＝DN*Gain+Offset
Wherein L ra is a radiation brightness value, gain is a correction Gain coefficient, offset is a correction Offset, DN is the DN value;
the process of obtaining the true reflectivity of the earth surface according to the radiance value comprises the following formula:
wherein p is the true reflectivity of the ground, L ra is the radiation brightness value, L path is the range radiation, E is the radiance of a ground object, and τ is the atmospheric transmissivity;
Obtaining a long-time sequence normalized vegetation index according to the ground surface true reflectivity;
constructing a stand age model according to the normalized vegetation index, and tracing and inverting the stand age of the stand to be detected according to the stand age model;
The process of constructing a stand age model according to the normalized vegetation index and inverting the stand age of the stand to be detected according to the stand age model is traced, and the formula is as follows:
Wherein M is the stand age of the stand to be measured, T c is a felling node, T p is a planting node, NDVI Tc is a normalized vegetation index at the felling node, For the normalized vegetation index at the planting node, NDVI i is the normalized vegetation index of the long-time series,/>Is the maximum value of normalized vegetation index of long time sequence,/>Is the minimum value of normalized vegetation index of long time sequence,/>Deriving a first derivative value at the felling node after a first order differentiation for a normalized vegetation index based on a long time series,/>Deriving a first derivative value at the planting node after a first derivative for a normalized vegetation index set based on the long time series; the felling node is the point at which the normalized vegetation index starts to change from a high value to a low value and shows a continuous descending trend, and the planting node is the lowest point of the normalized vegetation index of the long-time sequence.
2. The method for measuring the age information of the stand according to claim 1, further comprising the steps of, before the process of constructing the age model of the stand from the normalized vegetation index:
And filtering and smoothing the normalized vegetation index.
3. The method for measuring the age information of the stand according to claim 2, wherein the process of filtering and smoothing the normalized vegetation index comprises the steps of:
and filtering and smoothing the normalized vegetation index through a Savitzky-Golay filter, wherein the following formula is as follows:
wherein, For reconstructed data, y j+i is the original data, C i is the filter coefficient, N is the number of data of the sliding window under study, k is the width of the window, and n=2k+1.
4. A stand age information measuring apparatus, comprising:
The image acquisition module is used for acquiring remote sensing satellite images of the forest stand to be detected in a long-time sequence and acquiring corresponding ground surface true reflectivity according to each remote sensing satellite image;
The process for obtaining the corresponding ground surface true reflectivity according to each remote sensing satellite image comprises the following steps:
Converting DN value of the remote sensing satellite image into a radiation brightness value;
Obtaining the true reflectivity of the earth surface according to the radiation brightness value;
the process of converting DN value of the remote sensing satellite image into radiation brightness value is as follows:
Lra＝DN*Gain+Offset
Wherein L ra is a radiation brightness value, gain is a correction Gain coefficient, offset is a correction Offset, DN is the DN value;
the process of obtaining the true reflectivity of the earth surface according to the radiance value comprises the following formula:
wherein p is the true reflectivity of the ground, L ra is the radiation brightness value, L path is the range radiation, E is the radiance of a ground object, and τ is the atmospheric transmissivity;
The index acquisition module is used for acquiring a long-time sequence normalized vegetation index according to the ground true reflectivity;
The age measurement module is used for constructing a stand age model according to the normalized vegetation index and tracing and inverting the stand age of the stand to be measured according to the stand age model;
The process of constructing a stand age model according to the normalized vegetation index and inverting the stand age of the stand to be detected according to the stand age model is traced, and the formula is as follows:
Wherein M is the stand age of the stand to be measured, T c is a felling node, T p is a planting node, For the normalized vegetation index at the felling node,/>For the normalized vegetation index at the planting node, NDVI i is the normalized vegetation index of the long-time series,/>Is the maximum value of normalized vegetation index of long time sequence,/>Is the minimum value of normalized vegetation index of long time sequence,/>Deriving a first derivative value at the felling node after a first order differentiation for a normalized vegetation index based on a long time series,/>Deriving a first derivative value at the planting node after a first derivative for a normalized vegetation index set based on the long time series; the felling node is the point at which the normalized vegetation index starts to change from a high value to a low value and shows a continuous descending trend, and the planting node is the lowest point of the normalized vegetation index of the long-time sequence.
5. A computer storage medium having stored thereon computer instructions which when executed by a processor implement the stand age information measurement method of any of claims 1 to 3.
6. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the stand age information measurement method according to any one of claims 1 to 3 when the program is executed by the processor.
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| CN114612789B (en) * | 2022-03-25 | 2023-10-13 | 衡阳师范学院 | Method for extracting evergreen forest stand change through long-time sequence satellite remote sensing |
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